code: def forward(self, x, padding_mask=None, layer=None):
      x, layer_results = self.extract_features(x, padding_mask, layer)
  
      if self.layer_norm_first and layer is None:
          x = self.layer_norm(x)
  
      return x, layer_results
  
imports: import math
  import numpy
  from typing import Dict, Optional, Tuple
  import torch
  from torch import Tensor, nn
  import torch.nn.functional
  from torch.nn import LayerNorm, Parameter
  from lavis.models.beats.modules import GradMultiply, SamePad, get_activation_fn, GLU_Linear, quant_noise
label_desc: Use image node_id 2 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.forward(x, padding_mask, layer) and returns: x,  layer_results